def radius_timing_test(d=2,radius=0.1,tests=[VectorTree]):
from plastk.rand import uniform,seed
from plastk.utils import time_call
import time
for e in range(3,20):
for db_type in tests:
print
print "Testing",db_type
db = db_type(vector_len=d)
n = 2**e
print "Adding",n,"data points....",
for i in range(n):
x = uniform(0,1,(d,))
db.add(x,None)
print "done."
print "Average search search time...",
seed(0,0)
start = time.clock()
total_results = 0
for i in range(100):
results,dists = db.find_in_radius(uniform(0,1,(d,)),radius)
total_results += len(results)
end = time.clock()
print (end-start)/100
print "Average results size:", total_results/100.0
def k_nearest_timing_test(d=2,k=1,tests=[VectorTree]):
from plastk.rand import uniform,seed
from plastk.utils import time_call
import time
new_seed = time.time()
for e in range(3,20):
print '==================='
for db_type in tests:
print
print "Testing",db_type
db = db_type(vector_len=d)
n = 2**e
seed(int(new_seed),int(new_seed%1 * 1000000))
print "Adding",n,"data points....",
total_add_time = 0.0
for i in range(n):
x = uniform(0,1,(d,))
start = time.clock()
db.add(x,None)
end = time.clock()
total_add_time += end-start
print "done. Average add time = %4.3f ms." %((total_add_time/n)*1000)
print "Average search search time...",
seed(0,0)
print '%6.3f ms'% (1000*time_call(100,lambda: db.k_nearest(uniform(0,1,(d,)),k)))
def ckpt_restore_state(self,filename):
from plastk import pkl
ckpt = pkl.load(filename)
self.verbose("Restoring checkpoint state")
for a in self.ckpt_attribs:
self.verbose(a,' = ', ckpt[a])
setattr(self,a,ckpt[a])
rand.seed(*ckpt['rand_seed'])
self.env.sim = self.agent.sim = self
self.episode_data = NetCDFFile(self.episode_filename,'a')
if self.step_vars:
self.step_data = NetCDFFile(self.step_filename,'a')
return ckpt
def setUp(self):
rand.seed(0,0)
self.data = [(rand.uniform(0,1,(self.dim,)),None) for i in range(self.N)]
for x,y in self.data:
self.db.add(x,y)
def setUp(self):
rand.seed(0, 0)
self.data = [(rand.uniform(0, 1, (self.dim, )), None)
for i in range(self.N)]
for x, y in self.data:
self.db.add(x, y)
